• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

VeTra:一种基于RNA速度的轨迹推断工具。

VeTra: a tool for trajectory inference based on RNA velocity.

作者信息

Weng Guangzheng, Kim Junil, Won Kyoung Jae

机构信息

Department of Biology, The Bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark.

Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200 Copenhagen N, Denmark.

出版信息

Bioinformatics. 2021 Oct 25;37(20):3509-3513. doi: 10.1093/bioinformatics/btab364.

DOI:10.1093/bioinformatics/btab364
PMID:33974009
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8545348/
Abstract

MOTIVATION

Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge.

RESULTS

To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis.

AVAILABILITY AND IMPLEMENTATION

The Vetra is available at https://github.com/wgzgithub/VeTra.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

单细胞RNA测序(scRNAseq)数据的轨迹推断(TI)是解释细胞周期和发育等动态细胞过程的有力方法。然而,准确推断轨迹仍然具有挑战性。RNA速度的最新发展提供了一种在不依赖先验知识的情况下可视化细胞状态转变的方法。

结果

为了基于RNA速度进行TI并对细胞进行分组,我们开发了VeTra。通过应用余弦相似度和合并弱连接组件,VeTra从细胞转变方向识别细胞组。此外,VeTra从推断的轨迹中提出关键调节因子。VeTra是TI及后续分析的有用工具。

可用性和实现方式

VeTra可在https://github.com/wgzgithub/VeTra获得。

补充信息

补充数据可在《生物信息学》在线获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7c8/8545348/87cce68fd292/btab364f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7c8/8545348/9225ca23f203/btab364f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7c8/8545348/533109288ee6/btab364f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7c8/8545348/87cce68fd292/btab364f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7c8/8545348/9225ca23f203/btab364f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7c8/8545348/533109288ee6/btab364f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7c8/8545348/87cce68fd292/btab364f3.jpg

相似文献

1
VeTra: a tool for trajectory inference based on RNA velocity.VeTra:一种基于RNA速度的轨迹推断工具。
Bioinformatics. 2021 Oct 25;37(20):3509-3513. doi: 10.1093/bioinformatics/btab364.
2
scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data.scShaper:一种从单细胞 RNA-seq 数据中快速准确推断线性轨迹的集成方法。
Bioinformatics. 2022 Feb 7;38(5):1328-1335. doi: 10.1093/bioinformatics/btab831.
3
Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data.消除冗余性可改善单细胞转录组数据中的聚类和轨迹推断。
Bioinformatics. 2022 Jan 27;38(4):1045-1051. doi: 10.1093/bioinformatics/btab795.
4
VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories.VeloViz:用于可视化细胞轨迹的基于RNA速度信息的嵌入
Bioinformatics. 2022 Jan 3;38(2):391-396. doi: 10.1093/bioinformatics/btab653.
5
Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity.基于 RNA 速度的单细胞 RNA-seq 数据中高分辨率轨迹的推断。
Cell Rep Methods. 2021 Oct 25;1(6):100095. doi: 10.1016/j.crmeth.2021.100095.
6
CStreet: a computed Cell State trajectory inference method for time-series single-cell RNA sequencing data.CStreet:一种用于时间序列单细胞 RNA 测序数据的计算细胞状态轨迹推断方法。
Bioinformatics. 2021 Nov 5;37(21):3774-3780. doi: 10.1093/bioinformatics/btab488.
7
scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data.scHiCPTR:通过双图细化对单细胞Hi-C数据进行无监督伪时间推断
Bioinformatics. 2022 Nov 30;38(23):5151-5159. doi: 10.1093/bioinformatics/btac670.
8
Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference.基于线性微分方程和速度推断的单细胞 RNA-seq 数据基因调控推断。
Bioinformatics. 2020 Sep 15;36(18):4774-4780. doi: 10.1093/bioinformatics/btaa576.
9
Cell-connectivity-guided trajectory inference from single-cell data.基于细胞连接性的单细胞数据轨迹推断。
Bioinformatics. 2023 Sep 2;39(9). doi: 10.1093/bioinformatics/btad515.
10
Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics.基于熵的单细胞转录组学中过渡状态和细胞轨迹的推断。
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac225.

引用本文的文献

1
Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.RNA速度的范式、创新及生物学应用:全面综述
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf339.
2
scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy.scGRN-熵:利用单细胞数据和基于基因调控网络的转移熵推断细胞分化轨迹。
PLoS Comput Biol. 2024 Nov 25;20(11):e1012638. doi: 10.1371/journal.pcbi.1012638. eCollection 2024 Nov.
3
Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.

本文引用的文献

1
Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity.基于 RNA 速度的单细胞 RNA-seq 数据中高分辨率轨迹的推断。
Cell Rep Methods. 2021 Oct 25;1(6):100095. doi: 10.1016/j.crmeth.2021.100095.
2
Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells.使用dyngen(一种单细胞多模态模拟器)引领未来的组学分析。
Nat Commun. 2021 Jun 24;12(1):3942. doi: 10.1038/s41467-021-24152-2.
3
Preprocessing choices affect RNA velocity results for droplet scRNA-seq data.预处理选择会影响液滴 scRNA-seq 数据的 RNA 速度结果。
基于流形约束RNA速度模型的统计推断揭示了细胞周期速度调节。
Nat Methods. 2024 Dec;21(12):2271-2286. doi: 10.1038/s41592-024-02471-8. Epub 2024 Oct 31.
4
Spatial transition tensor of single cells.单细胞的空间转移张量。
Nat Methods. 2024 Jun;21(6):1053-1062. doi: 10.1038/s41592-024-02266-x. Epub 2024 May 16.
5
LVPT: Lazy Velocity Pseudotime Inference Method.LVPT:惰性速度拟时推断方法。
Biomolecules. 2023 Aug 12;13(8):1242. doi: 10.3390/biom13081242.
6
A robust and accurate single-cell data trajectory inference method using ensemble pseudotime.基于集成伪时间的稳健准确单细胞数据轨迹推断方法
BMC Bioinformatics. 2023 Feb 20;24(1):55. doi: 10.1186/s12859-023-05179-2.
7
Simulation-based inference of differentiation trajectories from RNA velocity fields.基于 RNA 速度场的分化轨迹的模拟推理。
Cell Rep Methods. 2022 Dec 19;2(12):100359. doi: 10.1016/j.crmeth.2022.100359.
8
RNA velocity unraveled.RNA 速度解析。
PLoS Comput Biol. 2022 Sep 12;18(9):e1010492. doi: 10.1371/journal.pcbi.1010492. eCollection 2022 Sep.
9
Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction.整合时间分辨的单细胞基因表达模式进行轨迹推断和疾病预测。
Genome Biol. 2022 Sep 5;23(1):186. doi: 10.1186/s13059-022-02749-0.
10
Systemic approaches using single cell transcriptome reveal that C/EBPγ regulates autophagy under amino acid starved condition.系统方法使用单细胞转录组揭示,在氨基酸饥饿条件下,C/EBPγ 调节自噬。
Nucleic Acids Res. 2022 Jul 22;50(13):7298-7309. doi: 10.1093/nar/gkac593.
PLoS Comput Biol. 2021 Jan 11;17(1):e1008585. doi: 10.1371/journal.pcbi.1008585. eCollection 2021 Jan.
4
TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data.TENET:利用传递熵重建基因网络,从单细胞转录组数据中揭示关键调控因子。
Nucleic Acids Res. 2021 Jan 11;49(1):e1. doi: 10.1093/nar/gkaa1014.
5
Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.Tempora:基于时间序列单细胞 RNA 测序数据的细胞轨迹推断。
PLoS Comput Biol. 2020 Sep 9;16(9):e1008205. doi: 10.1371/journal.pcbi.1008205. eCollection 2020 Sep.
6
Generalizing RNA velocity to transient cell states through dynamical modeling.通过动态建模将 RNA 速度推广到瞬时细胞状态。
Nat Biotechnol. 2020 Dec;38(12):1408-1414. doi: 10.1038/s41587-020-0591-3. Epub 2020 Aug 3.
7
Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.使用 Scribe 从耦合的单细胞表达动力学推断因果基因调控网络。
Cell Syst. 2020 Mar 25;10(3):265-274.e11. doi: 10.1016/j.cels.2020.02.003. Epub 2020 Mar 4.
8
Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression.MERFISH 技术进行空间转录组分析揭示了细胞内 RNA 区室化和细胞周期依赖性基因表达。
Proc Natl Acad Sci U S A. 2019 Sep 24;116(39):19490-19499. doi: 10.1073/pnas.1912459116. Epub 2019 Sep 9.
9
Comprehensive single cell mRNA profiling reveals a detailed roadmap for pancreatic endocrinogenesis.综合单细胞 mRNA 图谱揭示了胰腺内分泌发生的详细路线图。
Development. 2019 Jun 17;146(12):dev173849. doi: 10.1242/dev.173849.
10
A comparison of single-cell trajectory inference methods.单细胞轨迹推断方法比较。
Nat Biotechnol. 2019 May;37(5):547-554. doi: 10.1038/s41587-019-0071-9. Epub 2019 Apr 1.